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"Analisis survival telah banyak digunakan dalam berbagai bidang, termasuk aktuaria. Analisis survival hendak diketahui distribusi dari waktu hingga terjadi suatu kejadian yang diamati. salah satu parameter yang dapat menjelaskan distribusidari waktu hingga terjadi kejadian tersebut ..."

"Model prediksi kesintasan kanker prostat metastasis tulang sudah pernah dilakukan sebelumnya. Namun, model prediksi kesintasan kanker prostat metastasis tulang pra-terapi belum pernah dialukan sebelumnya. Tujuan penelitian ini adalah untuk menganalisis faktor-faktor klinis yang mempengaruhi ketahanan hidup (survival) pada kanker prostat dengan metastasis tulang serta mengembangkan nomogram prognostik ketahanan hidup pada pasien dengan kondisi tersebut. Terdapat 392 subyek dengan kanker prostat dengan metastasis tulang yang mendapat terapi Androgen Deprivation Therapy (ADT) dalam penelitian ini. Parameter pra-perawatan dianalisis menggunakan model cox-proportional untuk mengidentifikasi prediktor ketahanan hidup secara keseluruhan. Kovariat yang menunjukkan nilai signifikansi secara statistik pada analisis multivariat akan dipakai untuk membentuk nomogram. Model prediktor linier digunakan untuk mengembangkan nomogram. Nilai median ketahanan hidup keseluruhan adalah 40,3 bulan (95% CI: 32.2 - 48.5). Analisis univariat menunjukkan bahwa T-stage, Gleason Score, nilai antigen spesifik prostat inisial, dan jumlah lesi metastasis merupakan faktor-faktor prognostik independen terhadap angka ketahanan hidup keseluruhan. Semua prediktor ini tetap menunjukkan hasil yang signifikan secara statistik sebagai faktor prognostik independen pada analisis model multivariat cox-regression. Nomogram yang terbentuk dari faktor-faktor prediktor tersebut menunjukkan diskriminasi yan baik dalam memprediksi ketahanan hidup dalam 5 tahun dengan area under the curve (AUC) sebesar 0.69. Kesepakatan yang diterima dari probabilitas yang diamati dan diprediksi telah dinilai dalam plot kalibrasi. Nilai median ketahanan hidup keseluruhan adalah 40,3 bulan. Prediksi nomogram ini dapat berguna sebagai alat untuk memprediksi angka ketahanan hidup keseluruhan pada sebelum terapi kanker prostat metastasis, secara spesifik pada populasi Indonesia. Penelitian lebih lanjut dibutuhkan untuk memberikan validasi eksternal untuk mendukung penggunaan nomogram ini.

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A survival prognostic model of prostate cancer with bone metastasis had been done before. However, a prognostic model of pre-treatment prostate cancer with bone metastasis had not yet done. This study aims to analyze the clinical factors among bone-metastatic prostate cancer and their relationships with survival as well as to develop a prognostic nomogram for overall survival in patients with this condition. This study included 392 patients with bone metastatic prostate caner treated with androgen deprivation therapy. Pre-treatment parameters were analyzed using cox-proportional hazard model to identify the predictors of overall survival. Covariates, which showed statistical significance on multivariate analysis, were used to develop a nomogram. Linear predictor model was utilized to develop the nomogram. Median overall survival was 40.3 months (95% CI: 32.2 to 48.5). Univariate analysis showed that clinical T-stage, Gleason Score, initial prostate specific antigen value, and number of metastatic lesion were independent prognostic factors for OS. These predictors still remained significant as independent prognostic factors for overall survival following analysis using multivariate cox-regression model. The nomogram constructed from those prognostic factors showed good discriminaton for predicting the 5-year OS with an Area Under the Curve of 0.69. Acceptable agreement of the observed and predicted probabilites was observed in the calibration plot. The median overall survival of patient with bone metastatic prostate cancer was 40.3 months. The prediction nomogram might be a useful tool for predicting overall survival in pre-treatment bone metastatic prostate cancer, specifically among Indonesian patients. Further studies are needed to provide external validation to support the utilization of this nomogram."

"This greatly expanded third edition of Survival analysis : a self-learning text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. The third edition continues to use the unique "lecture-book" format of the first two editions with one new chapter, additional sections and clarifications to several chapters, and a revised computer appendix. The Computer Appendix, with step-by-step instructions for using the computer packages STATA, SAS, and SPSS, is expanded this third edition to include the software package R."

"Survival data consist of a single event for each population unit, namely, end of life, which is modeled with a life distribution. In contrast, many applications involve repeated-events data, where a unit may accumulate any number of events over time. Examples include the number and cost of repairs of products, the number and treatment costs of recurrent disease episodes in patients, and the number of childbirths to statisticians. This applied book provides practitioners with basic nonparametric methods for such data, particularly the plot of the estimate of the population mean cumulative function (MCF), which yields most of the information sought.Recurrent Events Data Analysis for Product Repairs, Disease Recurrences, and Other Applications is the first book to present a simple, unified theory that includes data on costs or other "values" of discrete events, not just the number of events. It surveys computer programs that calculate and plot the MCF estimate with confidence limits, shows their output, and explains how to interpret such plots. Many such calculations can be easily done with a pocket calculator or spreadsheet program. Also, the book introduces basic Poisson and Cox regression models and parametric models, including homogeneous and nonhomogeneous Poisson processes and renewal processes."

"Latar Belakang : Lupus Eritematosus Sistemik ( LES ) adalah penyakit reumatik autoimun yang ditandai adanya inflamasi luas yang melibatkan hampir seluruh sistem organ. Penyakit ini menyerang wanita muda dengan insiden puncak usia 15-40 tahun. Manifestasi klinik LES beragam tergantung organ yang terlibat. Risiko kematian pada pasien LES meningkat apabila tidak terdiagnosis dan tidak ditangani secara tepat. Tujuan : Mengetahui kesintasan pasien LES di RSCM beserta faktor-faktor yang memengaruhi kesintasan.Metode : Penelitian ini merupakan penelitian kohort retrospektif dengan menggunakan data rekam medis dari pasien yang terdiagnosis LES pertama kali pada tahun 2014-2019 di RSCM. Dilakukan analisis survival terhadap usia, jenis kelamin, anemia hemolitik, trombositopenia, NPSLE, anti ds-DNA tinggi, C3 dan C4 rendah, penggunaanglukokortikoid dosis tinggi, limfopenia, Anti Cardiolipin Antibody ( ACA ) positif, penyakit kardiovaskular, dan nefritis lupus. Dilakukan analisis multivariat dengan cox regression.Hasil: Terdapat 448 subjek yang diteliti. Kesintasan lima tahun pasien LES di RSCM adalah 88%. Rerata kesintasan 56 bulan (IK95% 55-57). Pada analisis multivariat, ditemukan bahwa NPSLE [HR 3,595 (IK95% 1,932-6,688)], kadar C3 dan C4 rendah [HR 2,501 (IK95% 1,330-4,701)], penyakit kardiovaskuler [HR 2,851 (IK95% 1,198-6,787)], dan anemia hemolitik [HR 2,106 (IK95% 1,008-4,404)] berpengaruh signifikan terhadap kesintasan 5 tahun pasien LES.Kesimpulan: Kesintasan kumulatif 5 tahun pasien LES di RSCM adalah 88% dengan neuropsikiatri lupus (NPSLE), kadar C3 dan C4 rendah, penyakit kardiovaskuler, dan anemia hemolitik berpengaruh signifikan terhadap kesintasan pasien LES di RSCM.

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Background : Systemic Lupus Erythematosus (SLE) is an autoimmune rheumatic disease characterized by widespread inflammation involving almost all organ systems. This disease attacks young women with a peak incidence aged 15-40 years. The clinical manifestations of SLE vary depending on the organs involved. The risk of death in SLE patients increases if it is not diagnosed and treated appropriately.

Objective : knowing the survival of SLE patients at RSCM along with the factors that influence survival.

Methods : This study is a retrospective cohort study using medical record data from patients diagnosed with SLE for the first time in 2014-2019 at RSCM. Survival analysis was carried out on age, gender, hemolytic anemia, trombocytopenia, NPSLE, high anti ds-DNA, low C3 and C4, use of high doses of glucocorticoids, lymphopenia, positive Anti-Cardiolipin Antibody (ACA), cardiovascular disease, and lupus nephritis. Multivariate analysis with cox

regression was carried out.

Results : There were 448 subjects studied. The 5 year survival of SLE patients at RSCM is 88%. Mean survival time 56 months (95%CI 55-57). In the multivariate analysis, it was found that NPSLE [HR 3,595 (95%CI 1,932-6,688)], low C3 dan C4 [HR 2,501 (95%CI 1,330-4,701)], cardiovascular disease [HR 2,851 (95% CI 1,198-6,787 )], dan hemolytic anemia [HR 2,106 (95% CI 1,008-4,404)] had a significant effect on 5 year SLE survival.

Conclusion : The 5 year survival cumulative of SLE patients at RSCM is 88% with neuropsychiatric lupus (NPSLE), low C3 dan C4, cardiovascular disease, dan hemolytic anemia have a significant effect on the survival of SLE patients at RSCM."

"Mortalitas pasien pneumonia di rumah sakit meningkat pada saat pandemi COVID-19. Perlu diidentifikasi faktor-faktor risikonya dari determinan biologi, gaya hidup, lingkungan dan pelayanan kesehatan. Penelitian ini menggunakan disain campuran studi kuantitatif kohort retrospektif dan studi kualitatif sequential explanatory. Sampling studi kuantitatif diambil secara acak sederhana dari rekam medis Mei 2020-Desember 2021 di RS dr. Cipto Mangunkusumo, Jakarta. Studi kualitatif berupa wawancara mendalam bersama enam orang informan. Terdapat 1945 subjek pneumonia dengan insiden kematian 34,1%. Determinan yang berhubungan dengan peningkatan risiko kematian adalah pneumonia berat (HR 1,8;IK95% 1,38-2,43), skor CCI ≥2 (HR 1,5;IK95% 1,16-2,08). komplikasi ≥2 (HR 5,9; 95%IK 2,9-11,9), intubasi (HR 1,6;IK95% 1,27-2,05) dan lama tunggu di IGD ≥8 jam (HR1,4;IK95% 1,12-1,63), tren kematian rawat inap meningkat seiring dengan bertambahnya usia. Risiko kematian lebih rendah pada subjek dengan infeksi utama selain paru (HR 0,4;IK95% 0,35-0,51), subjek yang mendapat perawatan intensif (HR 0,3;IK95% 0,25-0,41), terapi antikoagulan (HR 0,3;IK95% 0,27-0,44) dan terapi steroid pada pneumonia non-COVID-19 kondisi berat (0,7;IK95% 0,5-0,9). Ketangguhan rumah sakit terjaga dengan adanya kebijakan zonasi, penerapan prinsip mitigasi risiko, dan modulasi layanan. Beban finansial berkurang melalui donasi atau hibah. Kerentanan rumah sakit antara lain kerapuhan infrastruktur, kecepatan kembali ke layanan reguler lebih lambat, rasa takut tenaga kesehatan, dan triase pra-rumah sakit belum berjalan.Determinan biologi, lingkungan dan pelayanan kesehatan berhubungan dengan sintas rawat inap pasien pneumonia pada masa pandemi COVID-19. Ketahanan rumah sakit perlu dinilai dengan melihat dampak pandemi terhadap kematian pneumonia COVID-19 maupun pneumonia non-COVID-19.

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In-hospital mortality of pneumonia increased during the COVID-19 pandemic. It is necessary to identify risk factors from biological determinants, lifestyle, environment and health services. This research uses a mixed design of a retrospective cohort quantitative study and a sequential explanatory qualitative study. Quantitative subjects were selected using simple random sampling based on medical records May 2020-December 2021 at Dr. Cipto Mangunkusumo Hospital, Jakarta. In-depth interviews with six informants were performed. There were 1945 pneumonia subjects with a mortality incidence of 34,1%. Determinants associated with an increased mortality risk were severe pneumonia (HR 1,8; 95% CI 1,38-2,43), CCI score ≥2 (HR 1,5; 95% CI 1,16-2,08). complications ≥2 (HR 5,9; 95% CI 2,9-11,9), intubation (HR 1,6; 95% CI 1,27-2,05) and waiting time in the ER ≥8 hours (HR1,4 ;95% CI 1,12-1,63), the trend of inpatient mortality increases with increasing age. The risk of death was lower in subjects with primary infections other than lung (HR 0,4; 95% CI 0,35-0,51), subjects receiving intensive care (HR 0,3; 95% CI 0,25-0,41), anticoagulant therapy (HR 0,3; 95% CI 0,27-0,44) and steroid therapy in severe non-COVID-19 pneumonia (0,7; 95% CI 0,5-0,9). Hospital resilience is maintained by having zoning policies, implementing risk mitigation principles, and modulating services. Financial burden is reduced through donations or grants. Hospital vulnerabilities include infrastructure fragility, slower return to regular services, fear of health workers, and pre-hospital triage not yet in place. Biological, environmental and health service determinants are related to the survival rate of pneumonia patients during the COVID-19 pandemic. Hospital resilience needs to be assessed by looking at the impact of the pandemic on mortality from COVID-19 pneumonia and non-COVID-19 pneumonia."